Evolving Real-Time Local Agent Control for Large-Scale Multi-agent Systems

Control for agents situated in multi-agent systems is a complex problem. This is particularly true in hard, open, dynamic environments where resource, privacy, bandwidth, and computational limitations impose restrictions on the type of information that agents may share and the control problem solving options available to agents. The MQ or motivational quantities framework addresses these issues by evaluating candidate tasks based on the agent's organizational context and by framing control as a local agent optimization problem that approximates the global problem through the use of state and preference.

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